With the development of integrated circuit (IC) technology, the feature size of an IC device is decreasing. Many simplifications and assumptions have been made on actual devices by traditional device models which are based on semi-empirical and half-device physics. Such modeling methods were feasible in practice in the past. However, it will bring lots of new physical effects as device sizes continue to reduce; in this way, models will be complicated with a traditional modeling method, or even modeling for some new devices will become impossible.
There has been a method for modeling IC with artificial neural networks (ANNs) at present. ANNs are based on a collection of artificial neurons which is abstracted from information processing to establish simple models formed to be various networks with different connection methods. By learning from training samples, an ANN may obtain a corresponding “knowledge” and fit approximately whichever nonlinear function; therefore it can be applied to the modeling of IC devices. In the process of modeling using ANNs, many factors, including selection of training samples, training weights, initialization of thresholds, configuration of ANN and optimization of training algorithms, may have an effect on a final result. What is concerned herein is the selection of training samples. The size of the training samples should not be too small or too large.
Inaccurate learning by ANN may be caused by a too small sample size, affecting the accuracy of the final result.
Where the size of the training samples is too large, over-fitting may be produced, making the extrapolation and interpolation of ANN very bad, and leading to inaccurate predictions with the training samples. In this case, the burden of training increases, causing a much more time-consuming training, and even an exponential growing in time cost with a more complex ANN structure. Further, a too large training sample size may multiply test overhead and the cost for modeling.
Therefore, how to select training samples for modeling IC with ANNs is vital.